Signal processing method and receiver

ABSTRACT

A receiver selects, regarding each of signals corresponding to component elements of a transmission column vector, a number of transmission signal candidates equal to a number according to a predetermined parameter based on inter-signal point distances between a plurality of transmission signal candidates regarding the transmission column vector and the signal from among the plural transmission signal candidates. Then, the receiver reproduces the transmission signal by determining a unique set of transmission signal candidates regarding each component element of the transmission column vector based on the sum total of the inter-signal point distances between the selected transmission signal candidates and the signals.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Application No. 2010-259303 filed on Nov. 19, 2010 inJapan, the entire contents of which are hereby incorporated byreference.

FIELD

The embodiments discussed herein are related to a signal processingmethod and a receiver. For example, signal processing methods andreceivers which reproduce a plurality of transmission signals from aplurality of reception signals are included in the signal processingmethod and the receiver.

BACKGROUND

In recent years, investigation of a MIMO (Multiple Input MultipleOutput) technique has been and is being carried out energetically as anext generation communication technique.

In the MIMO system, a plurality of data streams are transmitted from atransmitter including a plurality of transmission antennas and areseparated and received by a receiver including a plurality of receptionantennas.

An example of a configuration of the MIMO system is illustrated in FIG.1.

The MIMO system 10 illustrated in FIG. 1 illustratively includes atransmitter 20 having a plurality of transmission antennas 21-1, 21-2, .. . , and 21-M (M is an integer equal to or higher than two) and areceiver 30 having a plurality of reception antennas 31-1, 31-2, . . . ,and 31-N (N is an integer equal to or higher than two). It is to benoted that, in the following description, where the transmissionantennas 21-1, 21-2, . . . and 21-M are not distinguished from eachother, they are described simply as transmission antennas 21, and, wherethe reception antennas 31-1, 31-2, . . . and 31-M are not distinguishedfrom each other, the reception antennas are described simply asreception antennas 31.

Further, in order to simplify the description, it is assumed as anexample that the transmitter 20 transmits M data streams equal to thenumber of the transmission antenna 21 while the receiver 30 receives Nreception signals equal to the number of the reception antenna 31.However, it is assumed that the following expression is satisfied:

M≦N

Here, if a vector x of M rows and one column including M data streams x₁to x_(M) as component elements, a channel matrix H of N rows and Mcolumns including propagation path gains h_(ζξ) between the ξth (1≦ξ≦M)transmission antennas and the ξth (1≦ζ≦N) reception antennas ascomponent elements, a vector y of N rows and one column includingreception signals y₁ to y_(N) as component elements and a vector n of Nrows and one column including noise n₁ to n_(N) as component elementsare defined, then the following expressions (1) and (2) are obtained:

$\begin{matrix}{y = {{Hx} + n}} & (1) \\{\begin{pmatrix}y_{1} \\y_{2} \\\vdots \\y_{N}\end{pmatrix} = {{\begin{pmatrix}h_{11} & h_{12} & \ldots & h_{1,M} \\h_{21} & h_{22} & \ldots & h_{2,M} \\\vdots & \vdots & \ddots & \vdots \\h_{N,1} & h_{N,2} & \ldots & h_{N,M}\end{pmatrix}\begin{pmatrix}x_{1} \\x_{2} \\\vdots \\x_{M}\end{pmatrix}} + \begin{pmatrix}n_{1} \\n_{2} \\\vdots \\n_{N}\end{pmatrix}}} & (2)\end{matrix}$

Meanwhile, as a method for separating a data stream on the receiver 30side, for example, an MMSE (Minimum Mean Square Error) method or an MLD(Maximum Likelihood Detection) method is available.

In the MMSE method, a data stream is separated by multiplying a receivedsignal by a predetermined coefficient based on mean square errorcriteria.

In the MLD method, a metric such as a square Euclidean distance iscalculated for combinations of all symbol replica candidates of aplurality of data streams and a combination of those symbol replicacandidates which minimizes the total of the metrics is determined as asignal after the data stream separation.

By the MLD method, an excellent reception performance can be obtained incomparison with a linear separation method such as the MMSE method andso forth. However, if a modulation multi-value number of the lth (1≦l≦M)transmission signal is represented by g_(l), then the number ofcombinations of the symbol replica candidates is calculated by

$\prod\limits_{l = 1}^{M}\; {g_{l}.}$

It is to be noted that, in the case of QPSK (Quadrature Phase ShiftKeying), g₁=4, in the case of 16QAM (16 Quadrature AmplitudeModulation), g₁=16, and in the case of 64QAM, g₁=64.

Therefore, there is a case that, as the modulation level and thetransmission data stream number increase, the number of times ofcalculation of the metric increases exponentially and the processingamount becomes enormous.

Therefore, various types of MLD methods have been proposed to reducemathematical operation amount.

For example, in Non-Patent Document 1 listed below, a QRM-MLD(complexity-reduced Maximum Likelihood Detection with QR decompositionand the M-algorithm) method which is a combination of QR decompositionand the M-algorithm has been proposed.

In the QRM-MLD method, a metric such as a square Euclidean distance ofall of symbol replica candidates with regard to surviving symbol replicacandidates in the preceding stage is calculated.

Where the number of surviving symbol replica candidates in the kth (k=1,2, . . . , M) stage is represented by S_(k), the number of times ofcalculation of the metric by the QRM-MLD method is calculated by thefollowing expression:

$g_{1} + {\sum\limits_{k = 1}^{M - 1}\; {g_{k + 1}S_{k}}}$

Meanwhile, in Non-Patent Document 2 listed below, an ASESS (AdaptiveSElection of Surviving Symbol replica candidates based on the maximumreliability) method in which a further reduction method of the number oftimes of calculation of a metric is applied to the QRM-MLD method isdisclosed.

According to the ASESS method, symbol replica candidates in each stageare ranked by quadrant decision and calculation of a metric is carriedout by the number of surviving symbol replica candidates in orderbeginning with a symbol replica candidate whose cumulative value (sumtotal) of the metric is low.

Accordingly, the number of times of calculation of the metric in theASESS method is calculated by the following expression:

$\sum\limits_{k = 1}^{M}\; S_{k}$

In this manner, in the ASESS method, the number of times of calculationof a metric increases linearly with respect to the number oftransmission data streams.

Further, in Patent Document 1 listed below, a method is proposed inwhich a plurality of transmission signal candidates are narrowed downstepwise by a predetermined number based on a proximity signal pointdata table which stores a corresponding relationship between estimatedtransmission signals of individual transmission systems and signalpoints which exhibit a short inter-signal point distance on atransmission constellation.

-   [Patent Document 1] Japanese Patent Laid-Open No. 2006-222872-   [Non-Patent Document 1] K. J. Kim and J. Yue, “Joint channel    estimation and data detection algorithms for MIMO-OFDM systems,” in    Proc. Thirty-Sixth Asilomar Conference on Signals, Systems and    Computers, pp. 1857-1861, November 2002-   [Non-Patent Document 2] K. Higuchi, H. Kawai, N. Maeda and M.    Sawahashi, “Adaptive Selection of Surviving Symbol Replica    Candidates Based on Maximum Reliability in QRM-MLD for OFCDM MIMO    Multiplexing,” Proc. of IEEE Globecom 2004, pp. 2480-2486, November    2004

SUMMARY

(1) According to an aspect of the embodiment, a method includes a signalprocessing method for a wireless communication system including atransmitter which transmits a wireless signal using M transmissionantennas and a receiver which receives the wireless signal using Nreception antennas, M and N individually being integers equal to orgreater than two, comprising performing QR decomposition of a channelmatrix between the transmitter and the receiver into products of anorthogonal matrix Q and an upper triangular matrix R, calculatingproducts Q^(H)y of Q^(H) which is Hermite conjugate of the orthogonalmatrix Q and a reception column vector y including N reception signalsreceived by the receiver as component elements, calculating a signalcorresponding to a component element of an Mth row of a transmissioncolumn vector x, which includes M transmission signals as componentelements, based on the calculated products Q^(H)y and the uppertriangular matrix R, selecting a plurality of transmission signalcandidates regarding the component element of the Mth row of thetransmission column vector x which are ranked in response to aninter-signal point distance with the signal calculated on aconstellation by carrying out a region decision on the constellationregarding the calculated signal, selecting a number of transmissionsignal candidates corresponding to predetermined parameters based on theinter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products Q^(H)y, the upper triangular matrix Rand the selected plural transmission signal candidates, and reproducingthe M transmission signals by determining a unique set of thetransmission signal candidates regarding the component elements of theMth to 1st rows of the transmission column vector x based on the sumtotal of the inter-signal point distances between the selectedtransmission signal candidates and the signals, can be used.

(2) According to an aspect of the embodiment, a method includes a signalprocessing method for a wireless communication system including atransmitter which transmits a wireless signal using M transmissionantennas and a receiver which receives the wireless signal using Nreception antennas, M and N individually being integers equal to orgreater than two, comprising generating, based on electric power valuesof column component elements which configure a channel matrix betweenthe transmitter and the receiver, a transformation channel matrix inwhich the column component elements are re-arranged, performing QRdecomposition of the generated transformation channel matrix intoproducts between an orthogonal matrix Q and an upper triangular matrixR, calculating a signal corresponding to a component element of an Mthrow of a transmission column vector x, which includes M transmissionsignals as component elements, based on the calculated products Q^(H)yand the upper triangular matrix R, selecting a plurality of transmissionsignal candidates regarding the component element of the Mth row of thetransmission column vector x which are ranked in response to aninter-signal point distance with the signal calculated on aconstellation by carrying out a region decision on the constellationregarding the calculated signal, selecting a number of transmissionsignal candidates corresponding to predetermined parameters based on theinter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products Q^(H)y, the upper triangular matrix Rand the selected plural transmission signal candidates, and reproducingthe M transmission signals by determining a unique set of thetransmission signal candidates regarding the component elements of theMth to 1st rows of the transmission column vector x based on the sumtotal of the inter-signal point distances between the selectedtransmission signal candidates and the signals, can be used.

(3) According to an aspect of the embodiment, an apparatus includes areceiver for a wireless communication system which includes atransmitter which transmits a wireless signal using M transmissionantennas and the receiver which receives the wireless signal using Nreception antennas, M and N individually being integers equal to orgreater than two, comprising a QR decomposition processor that performsQR decomposition of a channel matrix between the transmitter and thereceiver into products between an orthogonal matrix Q and an uppertriangular matrix R, a unitary transformer that calculates productsQ^(H)y between Q^(H) which is Hermite conjugate of the orthogonal matrixQ calculated by the QR decomposition processor and a reception columnvector y including N reception signals received by the receiver ascomponent elements, and a signal reproducer that calculates a signalcorresponding to a component element of the Mth row of a transmissioncolumn vector x, which includes M transmission signals as componentelements, based on the products Q^(H)y calculated by the unitarytransformer and the upper triangular matrix R, selects a plurality oftransmission signal candidates regarding component elements of the Mthrow of the transmission column vector x, which are ranked in response toan inter-signal point distance with the signal calculated on aconstellation by carry out a region decision on the constellationregarding the calculated signal, selects a number of transmission signalcandidates corresponding to predetermined parameters based oninter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products Q^(H)y, the upper triangular matrix Rand the selected plural transmission signal candidates, and reproducesthe M transmission signals by determine a unique set of the transmissionsignal candidates regarding the component elements of the Mth to 1strows of the transmission column vector x based on the sum total of theinter-signal point distances between the selected transmission signalcandidates and the signals, can be used.

The object and advantages of the embodiment will be realized andattained by means of the elements and combinations particularly pointedout in the claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the embodiment, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view illustrating an example of a configuration ofa MIMO system;

FIGS. 2A to 2D are diagrammatic views illustrating an example of asearch for a surviving path by an ASESS method;

FIG. 3 is a diagrammatic view illustrating another example of a searchfor a surviving path by the ASESS method;

FIG. 4 is a block diagram illustrating an example of a configuration ofa MIMO system according to an embodiment;

FIG. 5 is a block diagram illustrating an example of a configuration ofa MIMO stream separation section illustrated in FIG. 4;

FIG. 6 is a view illustrating an example of region decision for thefirst time where QPSK is used as a modulation method;

FIG. 7 is a view illustrating an example of a ranking tablecorresponding to the region decision illustrated in FIG. 6;

FIG. 8 is a view illustrating an example of region decision for thesecond time where the QPSK is used as a modulation method;

FIG. 9 is a view illustrating g an example of a ranking tablecorresponding to the region decision illustrated in FIG. 8;

FIGS. 10 to 12 are views illustrating an example of quadrant decisionwhere 64QAM is used as a modulation method;

FIG. 13 is a view illustrating an example of a table for surviving pathselection according to the embodiment;

FIG. 14 is a flow chart illustrating an example of operation of areceiver according to the embodiment;

FIG. 15 is a block diagram illustrating an example of a configuration ofa MIMO system according to a first modification;

FIG. 16 is a block diagram illustrating an example of a configuration ofa MIMO stream separation section illustrated in FIG. 15;

FIG. 17 is a flow chart illustrating an example of operation of areceiver according to the first modification;

FIG. 18 is a block diagram illustrating an example of a configuration ofa MIMO system according to a second modification;

FIG. 19 is a block diagram illustrating an example of a configuration ofa MIMO stream separation section illustrated in FIG. 18; and

FIG. 20 is a flow chart illustrating an example of operation of areceiver according to the second modification.

DESCRIPTION OF EMBODIMENTS

First, the ASESS method mentioned hereinabove is described. It is to benoted that, in order to simplify the description, a case in which M=N istaken as an example also here.

In the ASESS method, a channel matrix H is QR decomposed into a unitarymatrix Q and an upper triangular matrix R as given by the followingexpression (3):

$\begin{matrix}{H = {{QR} = {\begin{pmatrix}q_{11} & q_{12} & \ldots & q_{1,N} \\q_{21} & q_{22} & \ldots & q_{2,N} \\\vdots & \vdots & \ddots & \vdots \\q_{N,1} & q_{N,2} & \ldots & q_{N,N}\end{pmatrix}\begin{pmatrix}r_{11} & r_{12} & \ldots & r_{1,N} \\\; & r_{22} & \ldots & r_{2,N} \\\; & O & \ddots & \vdots \\\; & \; & \; & r_{N,N}\end{pmatrix}}}} & (3)\end{matrix}$

where 0 represents a zero matrix.

Then, if the reception signal y represented in the expressions (1) and(2) is multiplied by an Hermite conjugate Q^(H) of the unitary matrix Qfrom the left, then the following expressions (4) and (5) are obtained:

$\begin{matrix}{z = {{Q^{H}y} = {{{Q^{H}{QRx}} + {Q^{H}n}} = {{Rx} + n^{\prime}}}}} & (4) \\{\begin{pmatrix}z_{1} \\z_{2} \\\vdots \\z_{N}\end{pmatrix} = {{\begin{pmatrix}r_{11} & r_{12} & \ldots & r_{1,N} \\\; & r_{22} & \ldots & r_{2,N} \\\; & O & \ddots & \vdots \\\; & \; & \; & r_{N,N}\end{pmatrix}\begin{pmatrix}x_{1} \\x_{2} \\\vdots \\x_{N}\end{pmatrix}} + \begin{pmatrix}n_{1}^{\prime} \\n_{2}^{\prime} \\\vdots \\n_{N}^{\prime}\end{pmatrix}}} & (5)\end{matrix}$

In this manner, the reception signal can be orthogonalized.

First, in the first stage in the ASESS method, region decision on theconstellation is carried out regarding a row in the lowest stage of theexpression (5):

z _(N) /r _(N,N)

and ranking of candidate replicas C_(N,η) of a transmission signal x_(N)is carried out. It is to be noted, however, the following expression issatisfied:

η=1, . . . , g _(N)

Here, the number of candidate replicas of the ith rank in the rankingregarding the transmission signal x_(N) is represented by ρ⁽¹⁾(i).Further, a surviving path in the first stage is represented by Π_(l)^((l))(i)=ρ^((l))(i). However, where a surviving path is represented byΠ^((k))(i), this represents that the surviving path is the ith survivingpath in the kth stage. On the other hand, where a surviving path isrepresented byΠ_(κ) ^((k))(i), this represents the path in the κth stage(κ=1, 2, . . . , k) of the ith surviving path in the κth stage.

Further, where a surviving path is represented by Π_(a˜b) ^((k))(i)(where a<b), this represents a partial path from the ath stage to thebth stage of the ith surviving path in the kth stage.

For example, in the case of

Π⁽⁴⁾={(0,1,2,3),(0,2,1),(1,2,3,0),(2,0,0,1)}

the surviving path is represented by

Π⁽⁴⁾(2)=(0,2,1,1)

Π₃ ⁽⁴⁾(3)=3

Π_(1˜3) ⁽⁴⁾(1)=(0,1,2)

Further, the number of candidate replicas equal to the survivingcandidate number S₁ determined in the descending order of the rank inthe ranking in the first stage is represented by c_(N,Π) ₁ ₍₁₎ _((i))(i=1, 2, . . . , S₁).

Then, a number of candidate replicas equal to the surviving candidatenumber S₁ are selected in the descending order of the rank in theranking, and the selected candidate replicas are determined as survivingpaths in the first stage. Then, a metric such as a square Euclideandistance or the like is calculated.

Where the square Euclidean distance is applied as the metric of thesurviving paths, the metric is calculated by the following expression:

d ₁(Π₁ ⁽¹⁾(i)=|z _(N) −r _(N,N) _(C) _(N,Π) ₁ ₍₁₎ _((i))|²(i=1, 2, . . ., S ₁)

In the second stage, the candidate replicas c_(N,η) of the S₁ survivingpaths surviving in the first stage are cancelled (subtracted) from aunitary transformation signal z_(N−1) regarding the second row from thebottom in the expression (5). Then, results of the cancellation aredivided by diagonal component elements of the (N−1)th row of the uppertriangular matrix R as given by the following expression:

u _(N−1)(Π₁ ⁽¹⁾(i))=(z _(N−1) −r _(N−1,N) _(C) _(N,Π) ₁ ₍₁₎_((i)))/r_(N−1,N−1)(i=1, 2, . . . , S ₁)

Then, region decision of the results of the division is carried out andranking of the candidate replicas c_(N−1,η) of the transmission signalx_(N−1) is carried out.

It is to be noted that the number of a candidate replica of the jth rankin the ranking of u_(N−1)(Π₁ ⁽¹⁾(i)) is represented as

ρ_(Π₁⁽¹⁾(i))⁽²⁾(j).

Further, in the ASESS method, a surviving path after the second stage isadaptively selected in the following manner.

First, values of

e(i):=d ₁(Π₁ ^((i))(i))

P(i):=1

v:=1

are set as initial values.

Then, a candidate replica

${\rho_{\prod_{1}^{(1)}{(i_{\min})}}^{(2)}\left( {P\left( i_{\min} \right)} \right)},{i_{\min} = {\arg \left( {\min \left\lbrack {e(i)} \right\rbrack} \right)}}$

of the P(i_(min))th rank in the ranking of i_(min) with which e (i)exhibits a minimum value while P (i) is not P (i)=g_(N−1) is selected,and a cumulative metric up to the second stage represented by thefollowing expression (6) is calculated:

$\begin{matrix}{{d_{2}\left( {{\Pi_{1}^{(1)}\left( i_{\min} \right)},{\rho_{\Pi_{1}^{(1)}{(i_{\min})}}^{(2)}\left( {P\left( i_{\min} \right)} \right)}} \right)} = {{d_{1}\left( {\Pi_{1}^{(1)}\left( i_{\min} \right)} \right)} + {{z_{N - 1} - {r_{{N - 1},N}c_{N,{\Pi_{1}^{(1)}{(i_{\min})}}}} - {r_{{N - 1},{N - 1}}{c_{{N - 1},\rho_{\Pi_{1}^{(1)}{(i_{\min})}}^{(2)}}\left( {P\left( i_{\min} \right)} \right)}}}}^{2}}} & (6)\end{matrix}$

Then, the parameters are updated as given below:

e(i_(min)) := d₂(Π₁⁽¹⁾(i_(min)), ρ_(Π₁⁽¹⁾(i_(min)))⁽²⁾(P(i_(min))))P(i_(min)) := P(i_(min)) + 1v := v + 1

Then, the with surviving path in the second stage is determined by

Π₁⁽²⁾(v) = Π₁⁽¹⁾(i_(min))Π₂⁽²⁾(v) = ρ_(Π₁⁽¹⁾(i_(min)))⁽²⁾(P(i_(min)))

The processes described above are carried out until the number ofsurviving paths in the second stage becomes equal to a surviving pathnumber S₂.

In the kth (k=3, . . . , N) stage later than the second stage, candidatereplicas of S_(k−1) surviving paths surviving in the (k−1)th stage arecancelled from the kth reception signal Z_(N−k+1) from the bottom.

Then, results of the cancellation are divided by diagonal componentelements of the (N−k+1)th row of the upper triangular matrix R as givenby

${u_{{N - k + 1},i}\left( {\Pi^{({k - 1})}(i)} \right)} = {\left( {z_{N - k + 1} - {\sum\limits_{p = 1}^{k - 1}\; {r_{{N - k + 1},{N - p + 1}}c_{{N - p + 1},{\Pi_{p}^{({k - 1})}{(i)}}}}}} \right)/r_{{N - k + 1},{N - k + 1}}}$     (i = 1, 2, …  , S_(k − 1))

and then region decision is carried out for the results of the decision.Then, ranking of the candidate replicas of the transmission signalx_(N−k+1) is carried out.

The surviving paths in the kth stage are adaptively selected in thefollowing manner. First, as an initial condition,

e(i):=d _(k−1)(Π^((k−1))(i))

P(i):=1

v:=1

are set.

Then, a candidate replica

ρ_(Π^((k − 1))(i_(min)))^((k))(P(i_(min))), i_(min) = arg (min [e(i)])

of the P(i_(max))th rank in the ranking of i_(min) with which e(i)exhibits a minimum value while P(i) is not P(i)=g_(N−k+1) is selected,and a cumulative metric up to the second stage represented by thefollowing expression (7) is calculated:

$\begin{matrix}{{d_{k}\left( {{\Pi^{({k - 1})}\left( i_{\min} \right)},{\rho_{\Pi^{({k - 1})}{(i_{\min})}}^{(k)}\left( {P\left( i_{\min} \right)} \right)}} \right)} = {{d_{k - 1}\left( {\Pi^{({k - 1})}\left( i_{\min} \right)} \right)} + {\begin{matrix}{z_{N - k + 1} - {\sum\limits_{p = 1}^{k - 1}{r_{{N - k + 1},{N - p + 1}}c_{{N - p + 1},{\Pi_{p}^{({k - 1})}{(i)}}}}} -} \\{r_{{N - k + 1},{N - k + 1}}C_{{N - k + 1},{\rho_{\Pi^{({k - 1})}{(i_{\min})}}^{(k)}{({P{(i_{\min})}})}}}}\end{matrix}}^{2}}} & (7)\end{matrix}$

Then, the parameters are updated as given below:

e(i_(min)) := d_(k)(Π^((k − 1))(i_(min)), ρ_(Π^((k − 1))(i_(min)))^((k))(P(i_(min))))P(i_(min)) := P(i_(min)) + 1 v := v + 1

Then, the with surviving path in the kth stage is determined by

Π_(1 ∼ k − 1)^((k)) = Π^((k − 1))(i_(min))Π_(k)^((k))(v) = ρ_(Π^((k − 1))(i_(min)))^((k))(P(i_(min)))

The processes described above are carried out until the number ofsurviving paths in the kth stage becomes equal to the surviving pathnumber S_(k).

An example of a search for a surviving path according to the ASESSmethod is illustrated in FIGS. 2A to 2D.

As illustrated in FIG. 2A, a minimum value search of a cumulative metricis carried out regarding surviving paths #1 to #4 in the precedingstage. In the example of FIG. 2A, the surviving path #1 is detected.

Then, as illustrated in FIG. 2B, to the surviving path #1 detected bythe minimum value search, a candidate of the first rank in the rankingof the surviving path #1 is added (refer to a shaded portion in FIG.2B).

Then, as illustrated in FIG. 2C, the minimum value search is carried outfor the cumulative metric value of the surviving path #1 to which thecandidate of the first rank in the ranking is added and cumulativemetrics of the surviving paths #2 to #4. In this instance, the survivingpath #3 is selected and, a candidate of the first rank in the ranking ofthe surviving path #3 is added to the surviving path #3.

Similarly, as illustrated in FIG. 2D, the minimum value search of acumulative metric is carried out. In this instance, the surviving path#1 is selected and a candidate of the second rank in the ranking of thesurviving path #1 is added to the surviving path #1.

In this manner, in the ASESS method, an algorithm for adaptivelysearching for a surviving path is adopted. Therefore, a minimum valuesearch for a number of elements equal to that of surviving paths in thepreceding stage is carried out by a number of times equal to that ofsurviving paths in the present stage.

However, there is a case in which a magnitude comparison processnecessary for the minimum value search must be carried out by a greatnumber of times. The number of times of the magnitude comparison processin the kth stage is S_(k−1)S_(k).

Further, the search algorithm for a surviving path is a sequentialalgorithm which depends upon the magnitude of a cumulative metricregarding a candidates added in the past as illustrated in FIG. 3.

In the example illustrated in FIG. 3, a minimum value search is carriedout at time t1 and a candidate of the first rank in the ranking of thesurviving path #1 is added.

Then, the cumulative metric of the added candidate is calculated at nexttime t2, and which surviving path is to be selected subsequentlybranches depending upon a result of the calculation.

As indicated by a branch A of FIG. 3, where the cumulative metric of thesurviving path #3 exhibits a minimum value as a result of the minimumvalue search at time t2, a candidate of the first rank in the ranking ofthe surviving path #3 is selected at time t3.

On the other hand, as indicated by another branch B of FIG. 3, where thecumulative metric of the surviving path #1 exhibits a minimum value as aresult of the minimum value search, a candidate of the second rank inthe ranking of the surviving path #1 is added at time t3.

In such repetitive processing as described above, since the processingsubstance depends upon a preceding processing result, parallelization ofprocesses is difficult in inplementation by hardware or software.Therefore, in order to complete the processing within requiredprocessing time, it is demanded to increase the speed of the operationclock frequency. However, if the operation clock frequency becomes high,then the power supply voltage becomes high and leakage currentincreases, and therefore, increase of power consumption is invited.

Hereinafter, exemplary embodiments will be described with reference toaccompanying drawings. The following exemplary embodiments are merelyexamples and do not intend to exclude various modifications andvariations to the proposed method and/or apparatus that are notspecifically described herein. Rather, various modifications orvariations may be made to the embodiments (for example, by combining theexemplary embodiments) without departing from the scope and spirit ofthe proposed method and/or apparatus.

[1] Embodiment

(1.1) Configuration of the MIMO System

An example of a configuration of a MIMO system according to anembodiment is illustrated in FIG. 4. The MIMO system 100 illustrated inFIG. 4 illustratively includes a transmitter 200 and a receiver 300.

The transmitter 200 carries out a predetermined transmission process fortransmission data and transmits the transmission data, for which thepredetermined transmission process has been carried out, using M (M isan integer equal to or higher than two) transmission antennas.

To this end, the transmitter 200 illustratively includes transmissionantennas 201-1, 201-2, . . . , and 201-M, transmission sections 202-1,202-2, . . . , and 202-M, a modulation section 203, and an errorcorrection encoding section 204. It is to be noted that, in thefollowing description, where the transmission antennas 201-1, 201-2, . .. , and 201-M are not individually distinguished from each other, thetransmission antennas are described simply as transmission antennas 201,and, where the transmission sections 202-1, 202-2, . . . , and 202-M arenot individually distinguished from each other, the transmissionsections are described simply as transmission section 202.

The error correction encoding section 204 adds a predetermined errorcorrection code to transmission data. The predetermined error correctioncode includes, for example, a convolution code, a turbo code and soforth. The transmission data to which the error correction code is addedis inputted to the modulation section 203.

The modulation section 203 carries out a predetermined modulationprocess for the transmission data inputted thereto from the errorcorrection encoding section 204 described above. As the modulationmethod, for example, QPSK (Quadrature Phase Shift Keying), 16QAM(Quadrature Amplitude Modulation), 64QAM and so forth are available. Itis to be noted that, while the present embodiment is described in thefollowing description taking the QPSK, 16QAM or 64QAM as an example,this is an example to the end and it is not intended to limit themodulation method to any specific modulation method.

The modulation section 203 in the present embodiment separates thetransmission data, for which the predetermined modulation process hasbeen carried out, into a plurality of data streams and inputs theseparated data streams to the transmission section 202.

The transmission section 202 carries out a predetermined wirelesstransmission process for the signal inputted thereto from the modulationsection 203 and transmits the signal, for which the predeterminedwireless transmission process has been carried out, through the antenna201. The predetermined wireless transmission process includes, forexample, D/A (digital/analog) conversion, frequency conversion (upconvert), signal amplification and so forth.

On the other hand, the receiver 300 receives the signal transmittedthereto from the transmitter 200 and carries out a predeterminedreception process for the received signal to reproduce data.

To this end, the receiver 300 illustratively includes reception antennas301-1, 301-2, . . . , and 301-N (N is an integer equal to or higher thantwo), reception sections 302-1, 302-2, . . . , and 302-N, a demodulationsection 303, and an error correction decoding section 304. It is to benoted that, in the following description, where the reception antennas301-1, 301-2, . . . , and 301-N are not individually distinguished fromeach other, the reception antennas are described simply as receptionantennas 301, and, where the reception sections 302-1, 302-2, . . . ,and 302-N are not individually distinguished from each other, thereception sections are described simply as reception section 302.

The reception section 302 carries out a predetermined wireless receptionprocess for the signals received by the reception antennas 301. Thepredetermined wireless reception process includes signal amplification,frequency conversion (down convert), A/D (analog/digital) conversion andso forth. The signals for which the predetermined wireless receptionprocess has been carried out are inputted to the demodulation section303.

Here, a reception column vector y of N rows and 1 column which includesN reception signals received by the reception section 302 as componentelements is represented, using a transmission column vector x of M rowsand 1 column which includes M data streams x₁ to x_(M) as componentelements, a channel matrix H of N rows and M columns which includespropagation path gains h between the ξth (1≦ζ≦M) transmission antennasand the ζth (1≦ζ≦N) reception antennas as component elements and a noisecolumn vector n of N rows and 1 column which includes noise n_(l) ton_(N) as component elements, by the following expressions (8) and (9):

$\begin{matrix}{y = {{Hx} + n}} & (8) \\{\begin{pmatrix}y_{1} \\y_{2} \\\vdots \\y_{N}\end{pmatrix} = {{\begin{pmatrix}h_{11} & h_{12} & \ldots & h_{1,M} \\h_{21} & h_{22} & \ldots & h_{2,M} \\\vdots & \vdots & \ddots & \vdots \\h_{N,1} & h_{N,2} & \ldots & h_{N,M}\end{pmatrix}\begin{pmatrix}x_{1} \\x_{2} \\\vdots \\x_{M}\end{pmatrix}} + \begin{pmatrix}n_{1} \\n_{2} \\\vdots \\n_{N}\end{pmatrix}}} & (9)\end{matrix}$

The demodulation section 303 carries out a predetermined demodulationprocess for the signals received by the reception section 302 toreproduce data.

To this end, the demodulation section 303 illustratively includes achannel estimation section 305 and a MIMO stream separation section 306.

The channel estimation section 305 extracts a known signal such as apilot signal (reference signal) or the like from each of the receptionsignals and carries out a channel (propagation path) estimation processbased on the extracted pilot signals or the like to obtain a channelmatrix H. The estimated channel matrix H is inputted to the MIMO streamseparation section 306.

The MIMO stream separation section 306 carries out a data reproductionprocess hereinafter described using the signals received by thereception section 302 and the channel matrix H estimated by the channelestimation section 305 and carries out a predetermined demodulationprocess for the reproduced signals. The signal obtained by thepredetermined demodulation process is inputted to the error correctiondecoding section 304.

The error correction decoding section 304 carries out predeterminederror correction decoding for the stream separated by the MIMO streamseparation section 306.

Here, an example of a configuration of the MIMO stream separationsection 306 is illustrated in FIG. 5. The MIMO stream separation section306 illustrated in FIG. 5 illustratively includes a reception signaltransformation section 400, a MIMO demodulation section 500 and an LLR(Log Likelihood Ratio) calculation section 600.

The reception signal transformation section 400 carries out atransformation process for the reception signal y inputted thereto fromthe reception section 302 and the channel matrix H inputted thereto fromthe channel estimation section 305.

To this end, the reception signal transformation section 400illustratively includes a QR decomposition processing section 401 and aunitary transformation section 402.

The QR decomposition processing section 401 carries out QR decompositionfor the channel matrix H inputted thereto from the channel estimationsection 305 to decompose the channel matrix H into a unitary matrix Qand an upper triangular matrix R as given by the following expression(10):

$\begin{matrix}{H = {{QR} = {\begin{pmatrix}q_{11} & q_{12} & \ldots & q_{1,N} \\q_{21} & q_{22} & \ldots & q_{2,N} \\\vdots & \vdots & \ddots & \vdots \\q_{N,1} & q_{N,2} & \ldots & q_{N,N}\end{pmatrix}\begin{pmatrix}r_{11} & \; & r_{12} & \ldots & r_{1,N} \\\; & \; & r_{22} & \ldots & r_{2,N} \\\; & \; & \; & \ddots & \vdots \\\; & O & \; & \; & \; \\\; & \; & \; & \; & {r_{N,N}\;}\end{pmatrix}}}} & (10)\end{matrix}$

Here, by suitably selecting the unitary matrix Q, the diagonal componentelements of the upper triangular matrix R can be set to positive realnumbers. The unitary matrix Q is inputted to the unitary transformationsection 402 while the upper triangular matrix R is inputted to the MIMOdemodulation section 500. In particular, the QR decomposition processingsection 401 functions as an example of a QR decomposition processor thatperforms QR decomposition of the channel matrix H between thetransmitter 200 and the receiver 300 into products between theorthogonal matrix Q and the upper triangular matrix R.

The unitary transformation section 402 multiplies the reception columnvector y received by the reception section 302 by an Hermite conjugateQ^(H) of the unitary matrix Q obtained by the QR decompositionprocessing section 401 to obtain a unitary transformation vector z. Atthis time, a relationship represented by the following expressions (11)and (12) is satisfied between the unitary transformation vector z andthe transmission column vector x:

$\begin{matrix}{z = {{Q^{H}y} = {{{Q^{H}{QRx}} + {Q^{H}n}} = {{Rx} + n^{\prime}}}}} & (11) \\{\begin{pmatrix}z_{1} \\z_{2} \\\vdots \\z_{N}\end{pmatrix} = {{\begin{pmatrix}r_{11} & \; & r_{12} & \ldots & r_{1,N} \\\; & \; & r_{22} & \ldots & r_{2,N} \\\; & \; & \; & \ddots & \vdots \\\; & O & \; & \; & \; \\\; & \; & \; & \; & {r_{N,N}\;}\end{pmatrix}\begin{pmatrix}x_{1} \\x_{2} \\\vdots \\x_{N}\end{pmatrix}} + \begin{pmatrix}n_{1}^{\prime} \\n_{2}^{\prime} \\\vdots \\n_{N}^{\prime}\end{pmatrix}}} & (12)\end{matrix}$

The unitary transformation vector z is inputted to the MIMO demodulationsection 500. In particular, the unitary transformation section 402functions as an example of a unitary transformer that calculatesproducts Q^(H)y between Q^(H) which is the Hermite conjugate of theorthogonal matrix Q calculated by the QR decomposition processingsection 401 and the reception column vector y including N receptionsignals received by the receiver 300 as component elements.

The MIMO demodulation section 500 divides a data reproduction processinto N stages and carries out the divided processes. For example, theMIMO demodulation section 500 calculates a signal corresponding to acomponent element of the Mth row of the transmission column vector x,which includes M transmission signals as component elements, based onthe product Q^(H)y calculated by the unitary transformation section 402and the upper triangular matrix R calculated by the QR decompositionsection 401. The MIMO demodulation section 500 selects a plurality oftransmission signal candidates regarding the component element of theMth row of the transmission column vector x, which are ranked inresponse to an inter-signal point distance with the signal calculated onthe constellation by carry out region decision on a constellationregarding the calculated signal to. The MIMO demodulation section 500selects a number of transmission signal candidates corresponding topredetermined parameters based on the inter-signal point distancesbetween the plural transmission signal candidates and the signals fromthe plural transmission signal candidates regarding the componentelements of the (M−1)th to first rows of the transmission column vectorx by carry out the region decision on the constellation regarding thesignals corresponding to the component elements of the (M−1)th to firstrows of the transmission column vector x based on the calculated productQ^(H)y, upper triangular matrix R and selected plural transmissionsignal candidates described hereinabove. The MIMO demodulation section500 determines a unique set of ones of the transmission signalcandidates regarding the component elements of the Mth to 1st rows ofthe transmission column vector based on the sum total of theinter-signal point distances between the selected transmission signalcandidates and the signals. By executing the series of processes, theMIMO demodulation section 500 can function as an example of the signalreproduction section for reproducing M transmission signals.

To this end, the MIMO demodulation section 500 illustratively includes afirst stage processing section 501-1, a second stage processing section501-2, a third stage processing section 501-3, . . . , and an Nth stageprocessing section 501-N.

It is to be noted that the following description of the presentembodiment is given using an example in which the MIMO demodulationsection 500 carries out ranking for a plurality of transmission signalcandidates regarding the component element of the Mth row of thetransmission column vector x in the ascending order of the inter-signalpoint distance to the signal corresponding to the component element ofthe Mth row of the transmission column vector x and determines a uniqueset of ones of the transmission signal candidates regarding thecomponent elements of the Mth to first rows of the transmission columnvector so that the sum total of the inter-signal point distances betweenthe selected transmission signal candidates and the signals exhibits aminimum value.

The first stage processing section 501-1 takes charge of a first stageof the data reproduction process and determines a symbol replicacandidate of a transmission stream x_(N) in the expression (12)described above.

To this end, the first stage processing section 501-1 illustrativelyincludes a ranking determination section 502 and a metric calculationsection 503.

The ranking determination section 502 of the first stage processingsection 501-1 calculates, using the upper triangular matrix R outputtedfrom the QR decomposition processing section 401 and the unitarytransformation vector z outputted from the unitary transformationsection 402, the following expression:

$u_{N,\eta} = \frac{z_{N}}{r_{N,N}}$

derived from the lowest stage of the expression (12) given hereinabove.

Then, by carrying out predetermined region decision on the constellationregarding the solutions u_(N,η), the ranking is carried out in theascending order of the distances between the solutions u_(N,η) and thesignal points of the modulation method used for transmission of thestream x_(N).

It is to be noted that the modulation method used for the transmissionmay be configured such that the decision is carried out by the receiver300 using the data transmitted from the transmitter 200, or themodulation method to be used for the transmission may be determined inadvance while information regarding the modulation method is shared bythe transmitter 200 and the receiver 300.

Here, the ranking of signal points by the ranking determination section502 is described in detail below.

First, the positive or negative sign of the real part and the imaginarypart of each solution u_(N,η) is decided to decide to which quadrant thesolution u_(N,η) belongs. Then, the origin is moved to the center of thequadrant which is decided, by the first time decision, as a quadrant towhich the solution u_(N,η) belongs, and quadrant decision for the secondtime is carried out. By repetitively carrying out the processesdescribed above by N_(div) (at least log₄g₁) times, the region in whichthe solution u_(N,η) exists can be decided from among 2^(2N) ^(div)regions on the constellation of the modulation method used for thetransmission of the stream x_(N).

An example of quadrant decision in the case of N_(div)=1 using the QPSKas the modulation method is illustrated in FIG. 6. It is to be notedthat a region number is same as a number of a signal point included ineach region. Further, the solution u_(N,η) is represented by a starmark.

In the case of the example illustrated in FIG. 6, by carrying out thequadrant decision, it is decided that the solution u_(N,η) exists in aregion (first quadrant) of the region number 0.

Then, ranking of the signal points is carried out based on a result ofthe region decision described above. The signal points of the modulationmethod used for the transmission of the stream x_(N) are ranked in theascending order of the distance of the region decided by the regiondecision described above.

In the present embodiment, ranking of the signal points is determined byreferring to a ranking table stored in a table or the like.

An example of the ranking table is illustrated in FIG. 7. Referring to arow of the region number 0 in FIG. 7, a signal point of the first rankin the ranking is 00 (decimal: 0); a signal point of the second rank inthe ranking is 01 (decimal: 1); a signal point of the third rank in theranking is 10 (decimal: 2); and a signal point of the fourth rank in theranking is 11 (decimal: 3).

It is to be noted that, in the case of the example illustrated in FIG.6, while it is not decided which one of the signal points 10 (decimal:2) and 01 (decimal: 1) is nearer to the region decided by the regiondecision described above, alternatively it may be arbitrarily determinedin advance which of the signal points is to be placed in a higher rank.

Further, also by increasing the number of times of repetition of thequadrant decision to enhance the accuracy of the ranking, the processdescribed above can be made ready for the ranking.

An example of the quadrant decision where N_(div)=2 using the QPSK asthe modulation method is illustrated in FIG. 8.

In the case of the example illustrated in FIG. 8, it is decided by thequadrant decision that the solution u_(N,η) exists in a region of theregion number 2. Further, by referring to a row of the region number 2in the ranking table of FIG. 9, a signal point of the first rank in theranking is 00 (decimal: 0); a signal point of the second rank in theranking is 01 (decimal: 1); a signal point of the third rank in theranking is 10 (decimal: 2); and a signal point of the fourth rank in theranking is 11 (decimal: 3).

The series of operations of the quadrant decision is described withreference to FIGS. 10 to 12 taking the case of 64QAM as an example. Itis to be noted that N_(div)=3.

First, in the quadrant decision for the first time, decision of thepositive or negative sign of the real part and the imaginary part iscarried out. As a result, it is decided that both of the real part andthe imaginary part are negative as illustrated in FIG. 10, and it isdecided that the solution u_(N,η) exists in the third quadrant.

Then, since it is decided by the quadrant decision for the first timethat the solution u_(N,η) exists in the third quadrant, the origin ismoved to (−4/√{square root over (42)},−4/√{square root over (42)}).

For the movement of the origin, a coordinate which is to become a nextorigin may be subtracted from the solution u_(N,η). In particular, thesubtraction of:

u _(N,η) ⁽²⁾ ={Re(u _(N,η))+4/√{square root over (42)}}+j{Im(u_(N,η))−4/√{square root over (42)}}

is carried out.

Also as the quadrant decision for the second time, decision of thepositive or negative sign of the real part and the imaginary part ofu_(N,η) ⁽²⁾ is carried out similarly. As a result, the first quadrant isdetected as illustrated in FIG. 11. Therefore, the origin is moved to(−2/√{square root over (42)},−2/√{square root over (42)}) similarly asin the decision for the first time.

The quadrant decision for the third time is carried out similarly to thefirst and second decisions, and as a result, the second quadrant isdetected as illustrated in FIG. 12.

From the forgoing, it is decided that the solution u_(N,η) exists in aregion of the region number 110001 (decimal: 49).

Then, the ranking is determined from the ranking table stored in thetable or the like. Since the decided region number is 49, by referringto a ranking table in the case of 64QAM and N_(div)=3, it is decidedthat, for example, a region number of the first rank in the ranking is110001 (decimal: 49); a region number of the second rank in the rankingis 100001 (decimal: 33); a region number of the third rank in theranking is 110000 (decimal: 48); . . . .

It is to be noted that, while division is involved in the calculation ofthe solution u_(n,η) in the process described above, if an expression

u _(N,η) =z _(N)

is used and a coordinate to be used as the origin is set to

(−4r _(N,N)/√{square root over (42)},−4r _(N,N)/√{square root over(42)}),(−2r _(N,N)/√{square root over (42)},−2r _(N,N)/√{square rootover (42)})

then the division becomes unnecessary and the processing amount can bereduced.

Further, the number of the candidate replica of the ith rank in theranking in the first stage determined as described above is representedby p⁽¹⁾(i). A result of the ranking of the signal points is inputted tothe metric calculation section 503.

The metric calculation section 503 calculates, using the uppertriangular matrix R inputted from the QR decomposition processingsection 401, unitary transformation vector z inputted from the unitarytransformation section 401 and ranks of the signal points in the rankinginputted from the ranking determination section 502, a metric betweenthe unitary transformation signal z_(N) and the symbol replicacandidates of the transmission signal x_(N).

First, a number of candidate replicas equal to the surviving candidatenumber S₁ which have comparatively high ranks in the ranking obtained bythe ranking determination section 502 are determined as surviving paths.Here, the surviving paths in the first stage are given by:

Π₁ ⁽¹⁾(i):=ρ⁽¹⁾(i)(i=1, 2, . . . S ₁)

Then, a metric is calculated regarding the determined surviving paths.Where the square Euclidean distance is used as the metric, the metric iscalculated by the following expression (13):

d ₁(Π₁ ⁽¹⁾(i)=|z _(N) −r _(N,N) _(C) _(N,Π) ₁ ₍₁₎ _((i))|²(i=1, 2, . . ., S ₁)

Alternatively, the total of Manhattan distances given by the followingexpression (14) may be used as the metric.

d ₁ ^((manhattan))(Π₁ ⁽¹⁾(i))=|Re(z _(N) −r _(N,N) _(C) _(N,Π) ₁ ₍₁₎_((i)) |+|IM(z _(N) −r _(N,N) _(C) _(N,Π) ₁ ₍₁₎ _((i))|(i=1, 2, . . . S₁)  (14)

It is to be noted that, while the square Euclidean distance is used forcalculation of the metric in the following description, the presentembodiment is not limited to this but the Manhattan distance may be usedinstead. The calculated metric is inputted to a surviving path selectionsection 506 and a metric calculation section 507 of the second stageprocessing section 501-2.

The second stage processing section 501-2 takes charge of the secondstage of the data reproduction process and determines symbol replicacandidates of the transmission stream x_(N−1) in addition to thetransmission stream x_(N). To this end, the second stage processingsection 501-2 illustratively includes a parameter calculation section504, a ranking determination section 505, a surviving path selectionsection 506, a metric calculation section 507 and an average cumulativemetric calculation section 508.

The parameter calculation section 504 of the second stage calculates, inaccordance with the upper triangular matrix R inputted from the QRdecomposition section 401, a ratio

$\alpha = \frac{r_{{N - 1},{N - 1}}}{r_{N,N}}$

between diagonal component elements in the second row from the bottom ofthe upper triangular matrix R and diagonal component elements in thelowest stage. The calculated upper triangular matrix diagonal componentelement ratio α is inputted to the surviving path selection section 506.

The ranking determination section 505 cancels the symbol replicas of thesurviving paths in the first stage from the unitary transformationsignal z_(N−1) inputted thereto from the unitary transformation section402, and divides results of the cancellation by diagonal componentelements of the (N−1)th row of the upper triangular matrix R. Then, theranking determination section 505 carries out region decision of

${u_{N - 1}\left( {\Pi_{1}^{(1)}(i)} \right)} = \frac{z_{N - 1} - {r_{{N - 1},N}c_{N,{\Pi_{1}^{(1)}{(i)}}}}}{r_{{N - 1},{N - 1}}}$(i = 1, 2, …  , S₁)

obtained by the division and carries out ranking of the signal points.Since details of this are similar to those in the first stage,description of them is omitted.

It is to be noted that the number of a candidate replica of the jth rankin the ranking in the second stage with respect to the ith survivingpath Π⁽¹⁾(i) in the first stage is determined as

ρ_(Π⁽¹⁾)⁽²⁾(j).

A result of the ranking of the signal points is inputted to thesurviving path selection section 506.

The surviving path selection section 506 determines a selection methodfor surviving paths based on the upper triangular matrix diagonalcomponent element ratio α inputted thereto from the first parametercalculation section 504 and selects a number of surviving paths Π⁽²⁾(i)equal to the surviving path number S₂ in the second stage based on thedetermined selection method of surviving paths and the result of theranking inputted thereto from the ranking determination section 505.

Here, the surviving path selection method in a conventional technique isconsidered.

As described hereinabove with reference to FIG. 3, if the squareEuclidean distance of a path to be added is great, then the possibilityincreases that a different path may be selected as the surviving pathwhose cumulative metric value exhibits a minimum value after the path isadded. In particular, the possibility is high that also a path whosecumulative metric is great may be selected from among the survivingpaths till the preceding stage (refer to branch A in FIG. 3).

On the other hand, if the square Euclidean distance of a path to beadded is small, then the possibility increases that a cumulative metricvalue of the surviving path to which the path just described is addedmay exhibit a minimum value. In particular, the possibility is high thatthe path may be selected in order beginning with a path whose cumulativemetric is small from among the surviving paths till the preceding stage(refer to the branch B in FIG. 3).

Therefore, since the surviving path selection section 506 changes theselection destination of a path based on the upper triangular matrixdiagonal component element ratio α to decrease the number of times ofthe magnitude comparison process and does not successively select thesurviving path as in the ASESS method, the speed of the processing canbe increased by parallelization of the processes.

The cumulative metric value in the second stage is calculated by thefollowing expression (15). Here, the first term on the right side of theexpression (15) represents a square Euclidean distance of the pathsurviving in the first stage and the second term represents a squareEuclidean distance of the path to be added in the second stage. Further,the expression (15) can be transformed into the following expression(16):

$\begin{matrix}{{d_{2}\left( {\Pi^{(2)}(i)} \right)} = {{{z_{N} - {r_{N,N}c_{N,{\Pi_{1}^{(2)}{(i)}}}}}}^{2} + {{z_{N - 1} - {r_{{N - 1},N}c_{N,{\Pi_{1}^{(2)}{(i)}}}} - {r_{{N - 1},{N - 1}}c_{{N - 1},{\Pi_{2}^{(2)}{(i)}}}}}}^{2}}} & (15) \\{\mspace{79mu} {{d_{2}\left( {\Pi^{(2)}(i)} \right)} = {r_{N,N}^{2}\begin{Bmatrix}{{{\frac{z_{N}}{r_{N,N}} - c_{N,{\Pi_{1}^{(2)}{(i)}}}}}^{2} + \left( \frac{r_{{N - 1},{N - 1}}}{r_{N,N}} \right)^{2}} \\{{\frac{z_{N - 1} - {r_{{N - 1},N}c_{N,{\Pi_{1}^{(2)}{(i)}}}}}{r_{{N - 1},{N - 1}}} - c_{{N - 1},{\Pi_{2}^{(2)}{(i)}}}}}^{2}\end{Bmatrix}}}} & (16)\end{matrix}$

As can be recognized from the expression (16) given above, the squareEuclidean distance of the path to be added in the second stage varies inproportion to the ratio

$\frac{r_{{N - 1},{N - 1}}}{r_{N,N}}$

between the diagonal component elements at the lowest stage of the uppertriangular matrix R and the second diagonal component elements from thebottom calculated, that is, α.

Therefore, the number of paths to be added to a surviving path in thepreceding stage is varied based on the value α.

In particular, upon surviving path selection in the second stage, as thevalue α described above decreases, the number of paths which are to beselected from among the surviving paths whose cumulative metric in thefirst stage is small increases. On the contrary, as the value αincreases, the number of paths which are to be selected also from amongthe surviving paths whose cumulative metric in the first stage is greatincreases.

Or, the number of paths to be added to a surviving path in the precedingstage may be varied based on a value α².

Or else, the number of paths to be selected may be changed based on aratio

$\beta_{2} = \frac{r_{{N - 1},{N - 1}}}{\frac{1}{S_{1}}{\sum\limits_{i = 1}^{S_{1}}{d_{1}(i)}}}$

between an average value of the cumulative metric value in the firststage and the diagonal component elements of the second row from thebottom of the upper triangular matrix R.

In particular, as the value β₂ described above decreases, the number ofpaths which are to be selected from among the surviving paths whosecumulative metric in the first stage is small increases. On thecontrary, as the value β₂ increases, the number of paths which are to beselected also from among the surviving paths whose cumulative metric inthe first stage is great increases.

Or otherwise, the number of paths to be added to a surviving path in thepreceding stage may be varied based on the value β₂ ².

Or else, the number of paths to be selected may be changed based on aratio

$\beta_{2}^{\prime} = \frac{r_{{N - 1},{N - 1}}}{\sqrt{\frac{1}{S_{1}}{\sum\limits_{i = 1}^{S_{1}}{d_{1}(i)}}}}$

Or else, the number of paths to be selected may be changed based on avalue β′₂ ².

After the third stage, the number of selection paths can be changedbased on a ratio

$\beta_{k} = \frac{r_{{N - k + 1},{N - k + 1}}}{\frac{1}{S_{k - 1}}{\sum\limits_{i = 1}^{S_{k - 1}}{d_{k - 1}(i)}}}$

between an average value of the cumulative metric values till thepreceding stage (k−1th stage) and the kth diagonal component elementsfrom the bottom of the upper triangular matrix R. In particular, as thevalue β_(k) described above decreases, the number of paths which are tobe selected from among the surviving paths whose cumulative metric inthe (k−1)th stage is small increases. On the contrary, as the valuedescribed above increases, the number of paths which are to be selectedalso from among the surviving paths whose cumulative metric in the(k−1)th stage is great increases.

Or, the number of selection paths may be changed based on a ratio

$\beta_{k}^{\prime} = \frac{r_{{N - k + 1},{N - k + 1}}}{\sqrt{\frac{1}{S_{k - 1}}{\sum\limits_{i = 1}^{S_{k - 1}}{d_{k - 1}(i)}}}}$

Or, the number of selection paths may be changed based on a value β′_(k)².

In the present example, as an example of surviving path selection in thesecond stage, a surviving path selection table which previouslyindicates a relationship between values of the upper triangular matrixdiagonal component element ratio α and the numbers of paths to beselected is searched, and σ(i) candidate replicas of comparatively highranks in the ranking in the second stage are selected as surviving pathsregarding the surviving path having the ith lowest metric value in thefirst stage.

It is to be noted that the surviving path selection table is preparedsuch that, as the value α decreases, the number of paths which are to beselected from among the surviving paths whose cumulative metric in thepreceding stage is small increases but, as the value α increases, thenumber of paths which are to be selected also from among the survivingpaths whose cumulative metric in the preceding stage is great increases.

An example of the surviving path selection table where the number ofsurviving paths in the preceding stage is 16 and the number of survivingpaths in the present stage is 16 is illustrated in FIG. 13.

Where the value of α is equal to or higher than 0 but is lower than 1,eight paths are selected from among the surviving paths whose cumulativemetric in the preceding stage is the lowest; four paths are selectedfrom among the surviving paths whose cumulative metric in the precedingstage is the second lowest; two paths are selected from among thesurviving paths whose cumulative metric in the preceding stage is thethird lowest; and two paths are selected from among the surviving pathswhose cumulative metric in the preceding stage is the fourth lowest.

On the other hand, where the value α is equal to or higher than 15, onepath is selected from the 16 surviving paths in the preceding stage.

Here, an algorithm for the surviving path selection in the second stageis described in detail.

First, surviving paths in the first stage sorted in the ascending orderof the metric value d₁ in the first stage are determined asΠ^((1),asc)(i).

Then, surviving paths in the second stage are selected using analgorithm given as

  γ :=1 δ :=1 for (w:=1; w≦S₂; w:=w+1) {  Π₁ ⁽²⁾(w) := Π₁ ^((1),asc) (γ)  Π₂⁽²⁾(w) := ρ_(Π^((1), asc)(γ))⁽²⁾(δ)  δ := δ +1  if (w == σ(γ)) {  γ := γ +1   δ := 1  } }

Further, σ(i) paths of comparatively high ranks in the ranking may beselected from among the surviving paths of the ith rank in the rankingin the first stage. In particular, from the selection method describedabove, the sorting of the surviving paths in the first stage inaccordance with the magnitude of the metric value d₁ in the first stagemay be omitted to select surviving paths in the second stage asrepresented by

  γ := 1 δ := 1 for (w := 1; w≦S₂; w:=w+1) {  Π₁ ⁽²⁾(w) := Π₁ ⁽¹⁾ (γ)  Π₂⁽²⁾(w) := ρ_(Π⁽¹⁾(γ))⁽²⁾(δ)  δ := δ +1  if (w==σ(γ)) {   γ := γ +1  δ := 1  }  }

The surviving paths selected here are inputted to the metric calculationsection 507 and the ranking determination section 510 in the thirdstage.

The metric calculation section 507 calculates a metric regarding thesurviving paths selected by the surviving path selection section 506using the upper triangular matrix R inputted thereto from the QRdecomposition section 401 and the unitary transformation vector zinputted thereto from the unitary transformation section 402. Then, themetric calculation section 507 calculates, using the metric inputtedfrom the metric calculation section 503 in the first stage, a cumulativemetric represented by the following expression (17):

d ₂(Π⁽²⁾(i))=d ₁(Π₁ ⁽²⁾(i))+|z _(N−1) r _(N−1,N) _(C) _(N,Π) ₁ ₍₂₎_((i)) −r _(N−1,N−1) _(C) _(N−1,Π) ₂ ₍₂₎ _((i))|²  (17)

The calculated cumulative metric is inputted to the average cumulativemetric calculation section 508 in the second stage, a surviving pathdetermination section 511 and a metric calculation section 512 in thethird stage.

The average cumulative metric calculation section 508 calculates anaverage value of the cumulative metric value represented by thefollowing expression (18):

$\begin{matrix}{d_{2}^{({average})} = {\frac{1}{S_{2}}{\sum\limits_{i = 1}^{S_{2}}{d_{2}\left( {\Pi^{(2)}(i)} \right)}}}} & (18)\end{matrix}$

in order to use the same for surviving path selection in the thirdstage. The average value of the cumulative metric value is inputted to asecond parameter calculation section 509 in the third stage.

The third stage processing section 501-3 takes charge of thereproduction process in the third stage and can be implemented byreplacing the first parameter calculation section 504 in the secondstage with the second parameter calculation section 509.

The second parameter calculation section 509 calculates, using the uppertriangular matrix R inputted from the QR decomposition processingsection 401 and the average cumulative metric value inputted from theaverage cumulative metric calculation section 508 in the second stage, aratio

$\beta_{3} = \frac{r_{{N - 2},{N - 2}}}{d_{2}^{({average})}}$

between the upper triangular matrix diagonal component elements and theaverage cumulative metric. The calculated upper triangular matrixdiagonal component element average cumulative metric ratio β₃ isinputted to the surviving path selection section 511.

The surviving path selection section 511 determines a selection methodfor a surviving path based on the upper triangular matrix diagonalcomponent element average cumulative metric ratio β₃ inputted theretofrom the second parameter calculation section 509, and selects a numberof surviving paths Π⁽³⁾(i) equal to the surviving path number S₃ in thesecond stage based on the determined surviving path selection method andthe result of the ranking inputted thereto from the rankingdetermination section 510.

In the present example, as an example of surviving path selection in thekth stage after the third stage, a table which indicates a relationshipbetween values of the upper triangular matrix diagonal component elementaverage cumulative metric ratio β_(k) and numbers of paths to beselected in advance is searched to select χ(i) candidate replicas ofcomparatively high ranks in the ranking in the kth stage as survivingpaths regarding the surviving paths having the ith lowest metric valuein the k−1th stage.

It is to be noted that the surviving path selection table is producedsuch that, as the value β_(k) described above decreases, the number ofpaths which are to be selected from among the surviving paths whosecumulative metric in the preceding stage is small increases but, as thevalue β_(k) increases, the number of paths which are to be selected alsofrom among the paths whose cumulative metric in the preceding stage isgreat increases.

The kth (k=4, . . . , N) stage processing section after the fourth stageis configured similarly to the third stage processing section 501-3.Accordingly, the process described above is carried out up to the lastNth stage.

It is to be noted that, since there is no next stage to the last stage,the average cumulative metric calculation section 513 need not beprovided in the last stage. In particular, an Nth stage processingsection 501-N which takes charge of the MIMO demodulation process in theNth stage includes a second parameter calculation section 514, a rankingdetermination section 515, a surviving path selection section 516 and ametric calculation section 517. A cumulative metric calculated by themetric calculation section 517 in the Nth stage is inputted to an LLRcalculation section 600.

The LLR calculation section 600 calculates a bit LLR for eachtransmission stream. First, a minimum value of the cumulative metric issearched and a surviving path whose cumulative metric is equal to theminimum value is determined as the most-likely combination of symbols.The bit LLR of the nth bit of the lth stream x₁ is the differencebetween the total metric with regard to the most-likely symbolcombination and a minimum value of a cumulative metric with regard to asymbol having an inverse value of the nth bit of the most-likely symbol.

In particular, parameters are calculated first as given by the followingexpressions:

  ∏^((N), ML) = arg  min ⌊d_(N)(∏^((N))(i))⌋$\mspace{20mu} {{d_{l,\min}\left( {b_{n} = {{bit}\left( {\prod_{l}^{{(N)},{ML}}{,n}} \right)}} \right)} = {d\left( \prod^{{(N)},{ML}} \right)}}$${d_{l,\min}\left( {b_{n} = {{invbit}\left( {\prod_{l}^{{(N)},{ML}}{,n}} \right)}} \right)} = {\min\limits_{{{bit}{({\prod_{l}^{(N)}{,n}})}} = {{invbit}{({\prod_{l}^{{(N)},{ML}}{,n}})}}}\left\lfloor {d\left( \prod_{l}^{(N)} \right)} \right\rfloor}$

where l is a stream number, and bit(x,n) and invbit(x,n) are an nth bitvalue of x and an nth inverse bit value of x, respectively. The bit LLRgiven by the following expression (19) is calculated by using theparameters:

LLR _(l)(n)=d _(l,min)(b _(n)=1)−d _(l,min)(b _(n)=0)  (19)

It is to be noted that, while the bit LLR is determined as a differenceof the metric as described above, a square root may be used asrepresented by the following expression (20):

LLR _(l)(n)=√{square root over (d _(l,min)(b _(n)=1))}−√{square rootover (d _(l,min)(b _(n)=0))}  (20)

(1. 2) Operation of the Receiver According to the Embodiment

Here, an example of operation of the receiver 300 according to theembodiment is described with reference to a flow chart illustrated inFIG. 14.

First, a predetermined wireless reception process is carried out forsignals received by the reception antenna 301 by the reception section302, and resulting signals are inputted to the demodulation section 303(step S101).

Then, pilot signals and so forth transmitted by the transmitter areextracted from the signals obtained at step S101 and a channel matrix His estimated by the channel estimation section 305 of the demodulationsection 303 (step S102). The estimated channel matrix H is inputted tothe MIMO stream separation section 306.

The signals inputted to the MIMO stream separation section 306 areinputted first to the reception signal transformation section 400. Then,QR decomposition is carried out for the channel matrix H obtained atstep S102 by the QR decomposition processing section 401 of thereception signal transformation section 400 and a unitary matrix Q andan upper triangular matrix R are outputted (step S103).

Then, the reception signal vector y obtained at step S101 is multipliedby an Hermite conjugate of the unitary matrix Q obtained at step S103 bythe unitary transformation section 402 to calculate the unitarytransformation vector z (step S104).

Then, the upper triangular matrix R obtained at step S103 and theunitary transformation vector z obtained at step S104 are inputted tothe MIMO demodulation section 500, and a predetermined data reproductionprocess is carried out by the first stage processing section 501-1 tothe Nth stage processing section 501-N.

First, by the ranking determination section 502 of the first stageprocessing section 501-1, ranking of candidate replicas of thetransmission signal x_(N) is carried out using the upper triangularmatrix R obtained at step S103 and the unitary transformation vector zobtained at step S104 (S105).

Then, a metric is calculated regarding S₁ surviving replicas determinedin the descending order of the rank of the ranking obtained at step S105by the metric calculation section 503 (step S106).

Thereafter, the processing advances to the second processing section(step S107).

In the kth stage after the first stage, ranking of the candidatereplicas of the transmission signal x_(N−k+1) is first carried out bythe ranking determination section 505, 510 or 515 using the uppertriangular matrix R obtained at step S103, unitary transformation vectorz obtained at step S104 and surviving replicas in the k−1th stage (stepS108).

Then, it is decided whether or not the present process is carried out inthe second stage (step S109), and, if it is decided that the process iscarried out in the second stage (Yes route at step S109), then a ratio αbetween the second diagonal component elements from the bottom and thediagonal component elements in the lowest stage of the upper triangularmatrix R obtained at step S103 is calculated by the first parametercalculation section 504 (step S110). On the other hand, if it is decidedthat the processing already advances to the process at or after thethird stage (No route at step S109), then a ratio β between the diagonalcomponent elements of the upper triangular matrix R obtained at stepS103 and the average cumulative metric obtained in the k−1th stage iscalculated by the second parameter calculation section 509 or 514 (stepS111).

Then, the number of surviving paths is determined based on the ratio αobtained at step S110 or the ratio β obtained at step S111 by thesurviving path selection sections 506, 511 or 516, and surviving pathsin the kth stage are selected based on the determined number ofsurviving paths and the ranking obtained at step S108 (step S112).

Then, a metric and a cumulative metric are calculated regarding thesurviving paths selected at step S112 by the metric calculation section507, 512 or 517 (step S113).

Then, it is decided whether or not the present process is carried out inthe Nth stage (step S114), and, if it is decided that the process iscarried out in the Nth stage (Yes route at step S114), then theprocessing advances to step S117. On the other hand, if it is decidedthat the present process is not carried out in the Nth stage (No routeat step S114), then an average value of the cumulative metric obtainedat step S113 is calculated by the average cumulative metric calculationsection 508 or 513 (step S115).

At the next step, the processing advances to the next stage (step S116).

Then, it is decided whether or not the processes till the Nth stage areincomplete (step S117), and, if it is decided that the processes tillthe Nth stage are incomplete (Yes route at step S117), then theprocessing advances to step S108 and the processes from step S108 tostep S116 are carried out till the Nth stage. On the other hand, if itis decided that the processes till the Nth stage are not incomplete,that is, are completed already (No route at step S117), then a bit LLRis calculated regarding each transmission stream by the LLR calculationsection 600 (step S118).

Then, predetermined error correction decoding is carried out for thestreams separated by the MIMO stream separation section 306 by the errorcorrection decoding section 304 (step S119).

By carrying out the surviving path selection by means of the survivingpath selection section 506, 511 or 516 based on the result of thecalculation by the first parameter calculation section 504 or the secondparameter calculation section 509 or 514 as described above, the signalprocessing amount upon data reproduction can be reduced.

[2] First Modification

An example of a configuration of a MIMO system according to the firstmodification is illustrated in FIG. 15. It is to be noted that, in FIG.15, since like elements to those in the embodiment described above aredenotedby like reference characters, detailed description of the likeelements is omitted.

As illustrated in FIG. 15, in the first modification, a MIMO streamseparation section 306A is provided.

An example of a configuration of the MIMO stream separation section 306Aaccording to the first modification is illustrated in FIG. 16. It is tobe noted that, also in FIG. 16, like elements to those in the embodimentdescribed above are denoted by like reference characters, and therefore,detailed description of the like elements is omitted.

As illustrated in FIG. 16, in the first modification, the MIMO streamseparation section 306A illustratively includes a channel rankingsection 700.

The channel ranking section 700 receives a channel matrix H inputtedfrom the channel estimation section 305 to calculate a sum

$P_{f} = {\sum\limits_{Ϛ = 1}^{N}{h_{\zeta \; f}}^{2}}$

of the power of fth column component elements of the channel matrix H,and row vectors of the channel matrix are re-arranged so that they arearranged in the descending order of the value P_(f) from the right.

As an example, a case in which the channel matrix H of three rows andthree columns given by the following expression (21) is inputted to thechannel ranking section 700 is described.

$\begin{matrix}{H = \begin{pmatrix}h_{11} & h_{12} & h_{13} \\h_{21} & h_{22} & h_{23} \\h_{31} & h_{32} & h_{33}\end{pmatrix}} & (21)\end{matrix}$

For example, it is assumed that, as a result of the calculation of thevalue P_(f), a relationship of P₂<P₃<P₁ is obtained. Then, by thechannel ranking section 700, re-arrangement of the column vectors iscarried out for the channel matrix H given by the expression (21) givenhereinabove so that the vectors are arranged in the descending order ofthe value P_(f) from the right thereby to obtain a channel matrix H′given by the following expression (22):

$\begin{matrix}{H^{\prime} = \begin{pmatrix}h_{12} & h_{13} & h_{11} \\h_{22} & h_{23} & h_{21} \\h_{32} & h_{33} & h_{31}\end{pmatrix}} & (22)\end{matrix}$

The channel matrix H′ for which the re-arrangement has been carried outis inputted to the reception signal transformation section 400.

In particular, the channel ranking section 700 functions as an exampleof a channel matrix transformation section for generating, based onpower values of column component elements which configure a channelmatrix between the transmitter 200 and the receiver 300, a transformedchannel matrix in which column component elements are re-arranged.

It is to be noted that, in the present modification, as an example, acase is described in which the channel ranking section 700 carries outre-arrangement of column component elements in the descending order ofthe sum total of the power values of the column component elements whichconfigure the channel matrix between the transmitter 200 and thereceiver 300. However, the present invention is limited to this. Forexample, the channel ranking section 700 may carry out re-arrangement ofthe column component elements which configure the channel matrix basedon a signal to noise ratio for each reception signal.

An example of a flowchart according to the first modification is shownin FIG. 17.

First, a predetermined wireless reception process is carried out by thereception section 302 for signals received by the reception antennas 301and then results of the process are inputted to the demodulation section303 (step S201).

Then, pilot signals transmitted from the transmitter or the like areextracted from the signals obtained at step S201 and a channel matrix His estimated by the channel estimation section 305 of the demodulationsection 303 (step S202). The estimated channel matrix H is inputted tothe MIMO stream separation section 306A.

Then, re-arrangement of column vectors is carried out for the channelmatrix H obtained at step S202 by the channel ranking section 700 of theMIMO stream separation section 306A (step S203). A channel matrix H′obtained by carrying out the re-arrangement described above is inputtedto the reception signal transformation section 400.

Then, QR decomposition is carried out for the channel matrix H′ obtainedat step S203 by the QR decomposition processing section 401 of thereception signal transformation section 400, and thereafter, the processν illustrated in FIG. 14 is carried out similarly as in the embodimentdescribed above.

With the first modification described above, since the diagonalcomponent elements r_(N,N) of the Nth row of the upper triangular matrixR obtained after the QR decomposition process by the QR decompositionprocessing section 401 can take a high value, the influence of noise canbe reduced in the calculation of the value u_(N,η) in the first stageprocessing section 501-1 and symbol replica candidates can be selectedmore accurately.

Further, since, in the succeeding stages, symbol replica candidates areselected based on the result of the selection in the first stage, alsosymbol replica candidates in the succeeding stages can be selectedaccurately. As a result, the data reproduction accuracy of the receiver300 can be enhanced.

[3] Second Modification

Further, while, in the embodiment described above, the ratio α of thediagonal component elements of the upper triangular matrix R is used forsurviving path selection in the second stage, the ratio between theaverage cumulative metric value and the diagonal component elements ofthe upper triangular matrix R till the preceding stage may be usedsimilarly as in the processes at and after the third stage. Therefore,in the second embodiment, an upper triangular matrix diagonal componentelement average cumulative metric ratio β₂ is used for surviving pathselection in the second stage.

An example of a configuration of a MIMO system according to the secondmodification is illustrated in FIG. 18. It is to be noted that, in FIG.18, like elements to those in the embodiment described above are denotedby like reference characters, and therefore, detailed description of thelike elements is omitted.

As illustrated in FIG. 18, in the second modification, a MIMO streamseparation section 306B is provided.

An example of a configuration of the MIMO stream separation section 306Baccording to the second modification is illustrated in FIG. 19. It is tobe noted that, also in FIG. 19, like elements to those in the embodimentdescribed above are denoted by like reference characters, and therefore,detailed description of the like elements is omitted.

The average cumulative metric calculation section 518 of the first stageprocessing section 501-1 calculates an average cumulative metric givenby the following expression (23) using a metric determined by theexpression (13) given hereinabove and inputted from the metriccalculation section 503:

$\begin{matrix}{d_{1}^{({average})} = {\frac{1}{S_{1}}{\sum\limits_{i = 1}^{S_{1}}{d_{1}\left( {\prod^{(1)}(i)} \right)}}}} & (23)\end{matrix}$

The calculated average cumulative metric is inputted to the secondparameter calculation section 519 of the second stage processing section501-2.

The second parameter calculation section 519 calculates, using anaverage cumulative metric inputted from the average cumulative metriccalculation section 518, a ratio β₂ given by the following expression(24) between the upper triangular matrix diagonal component elements andthe average cumulative metric:

$\begin{matrix}{\beta_{2} = \frac{r_{{N - 1},{N - 1}}}{d_{1}^{({average})}}} & (24)\end{matrix}$

The other processes are similar to those of the embodiment describedhereinabove.

An example of a flow chart of the present modification is illustrated inFIG. 20.

First, a predetermined wireless reception process is carried out by thereception section 302 for signals received by the reception antennas 301and then resulting signals are inputted to the demodulation section 303(step S301).

Then, pilot signals transmitted from the transmitter and so forth areextracted from the signals obtained at step S301 and a channel matrix His estimated by the channel estimation section 305 of the demodulationsection 303 (step S302). The estimated channel matrix H is inputted tothe MIMO stream separation section 308.

The signal inputted to the MIMO stream separation section 308 isinputted first to the reception signal transformation section 400. Then,QR decomposition is carried out for the channel matrix H obtained atstep S302 by the QR decomposition processing section 401 of thereception signal transformation section 400, and a unitary matrix Q andthe upper triangular matrix R are outputted from the QR decompositionprocessing section 401 (step S303).

Then, the reception signal vector y obtained at step S301 is multipliedby an Hermite conjugate of the unitary matrix Q obtained at step S303 bythe unitary transformation section 402 to calculate a unitarytransformation vector z (step s304).

Then, the upper triangular matrix R obtained at step S303 and theunitary transformation vector obtained at step S304 are inputted to theMIMO demodulation section 500 and predetermined data reproductionprocesses are carried out by the first to Nth stage processing sections501-1 to 501-N.

First, ranking of candidate replicas of the transmission signal x_(N) iscarried out by the ranking determination section 502 of the first stageprocessing section 501-1 using the upper triangular matrix R obtained atstep S303 and the unitary transformation vector z obtained at step S304(step S305).

Then, a metric is calculated regarding S₁ surviving replicas determinedin the descending order of the rank in the ranking obtained at step S305by the metric calculation section 503 (step S306).

Then, an average value of the cumulative metric is calculated by theaverage cumulative metric calculation section 518 in the first stageusing the metrics obtained at step S306 (step S307).

Thereafter, the processing advances to the second stage processingsection (step S308).

In the kth stage after the second stage, ranking of the candidatereplicas of the transmission signal x_(N−k+1) is carried out first bythe ranking determination section 505 or 515 using the upper triangularmatrix R obtained at step S303, unitary transformation vector z obtainedat step S304 and surviving replicas in the k−1th stage (step S309).

Then, a ratio β between the diagonal component elements of the uppertriangular matrix R obtained at step S303 and the average cumulativemetric obtained in the k−1th stage is calculated by the second parametercalculation section 514 or 519 (step S310).

Then, the number of surviving paths is determined based on the rankingobtained at step S309 and the ratio β obtained at step S310 andsurviving paths in the kth stage are selected based on the determinedsurviving path number and the ranking obtained at step S308 by thesurviving path selection section 506 or 516 (step S311).

Then, a metric and a cumulative metric are calculated regarding thesurviving paths selected at step S311 by the metric calculation section507 or 517 (step S312).

Then, it is decided whether or not the present process is carried out inthe Nth stage (step S313), and, if it is decided that the presentprocess is carried out in the Nth stage (Yes route at step S313), thenthe processing advances to step S316. On the other hand, if it isdecided that the present process is not carried out in the Nth stage (Noroute at step S313), then an average value of the cumulative metricobtained at step S312 is calculated by the average cumulative metriccalculation section 508 (step S314).

At a next step, the processing advances to the next stage (step S315).

Then, it is decided whether or not the processes till the Nth stage areincomplete (step S316), and, if it is decided that the processes tillthe Nth stage are incomplete (Yes route at step S316), then theprocessing advances to step S309 and the processes from step S309 tostep S315 are carried out till the Nth stage. On the other hand, if itis decided that the processes till the Nth stage are not incomplete,that is, are completed already (No route at step S316), then a bit LLRis calculated regarding each transmission stream by the LLR calculationsection 600 (step S317).

Then, predetermined error correction decoding by the error correctiondecoding section 304 is carried out for the streams separated by theMIMO stream separation section 306B (step S318).

With the second modification described above, since the stages ratherthan the second stage processing section 501-2 have configurationssimilar to each other, the processing substances in the MIMOdemodulation section 500 can be simplified and the fabrication cost forthe receiver 300B can be reduced significantly.

[4] Others

It is to be noted that the configurations and functions of thetransmitter 200, receiver 300, receiver 300A and receiver 300B describedabove may be adopted or abandoned as occasion demands, or may becombined and used suitably. In other words, the configurations and thefunctions described above may be adopted or abandoned or may be combinedand used suitably in order that the function of the present inventioncan be exhibited.

Further, while an example is described in which ranking of signal pointsis carried out by referring to the ranking table stored in a table orthe like after region decision of the values u_(N,η) is carried out inthe ranking of signal points by the ranking determination section 502,505, 510 or 515 in the embodiment described above, the ranking of signalpoints may be carried out in the ascending order of the distance to thevalues u_(N,η). In particular, ranking of signal points may be carriedout in the ascending order of the distance using distances between thevalue u_(N,η) and the signal points calculated by the rankingdetermination section 502, 505, 510 or 515.

Further, while the embodiment is described illustratively taking a casein which QPSK or 64QAM is used as the modulation method for the rankingof signal points in the embodiment described above, the embodiment isnot limited to the modulation methods but can be carried out also in acase in which a different modulation method is used.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments have beendescribed in detail, it should be understood that the various changes,substitutions, and alterations could be made hereto without departingfrom the spirit and scope of the invention.

1. A signal processing method for a wireless communication systemincluding a transmitter which transmits a wireless signal using Mtransmission antennas and a receiver which receives the wireless signalusing N reception antennas, M and N individually being integers equal toor greater than two, the signal processing method comprising: performingQR decomposition of a channel matrix between the transmitter and thereceiver into products of an orthogonal matrix Q and an upper triangularmatrix R; calculating products Q^(H)y of Q^(H) which is Hermiteconjugate of the orthogonal matrix Q and a reception column vector yincluding N reception signals received by the receiver as componentelements; calculating a signal corresponding to a component element ofan Mth row of a transmission column vector x, which includes Mtransmission signals as component elements, based on the calculatedproducts QHy and the upper triangular matrix R; selecting a plurality oftransmission signal candidates regarding the component element of theMth row of the transmission column vector x which are ranked in responseto an inter-signal point distance with the signal calculated on aconstellation by carrying out a region decision on the constellationregarding the calculated signal; selecting a number of transmissionsignal candidates corresponding to predetermined parameters based on theinter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products Q^(H)y, the upper triangular matrix Rand the selected plural transmission signal candidates; and reproducingthe M transmission signals by determining a unique set of thetransmission signal candidates regarding the component elements of theMth to 1st rows of the transmission column vector x based on the sumtotal of the inter-signal point distances between the selectedtransmission signal candidates and the signals.
 2. The signal processingmethod according to claim 1, wherein the predetermined parametersinclude a ratio between an average value of the sum totals of theinter-signal point distances and a diagonal component element of theupper triangular matrix R.
 3. The signal processing method according toclaim 1, wherein the predetermined parameters include a ratio between adiagonal component element of the (M−1)th row of the upper triangularmatrix R and a diagonal component element of the Mth row of the uppertriangular matrix R.
 4. A signal processing method for a wirelesscommunication system including a transmitter which transmits a wirelesssignal using M transmission antennas and a receiver which receives thewireless signal using N reception antennas, M and N individually beingintegers equal to or greater than two, the signal processing methodcomprising: generating, based on electric power values of columncomponent elements which configure a channel matrix between thetransmitter and the receiver, a transformation channel matrix in whichthe column component elements are re-arranged; performing QRdecomposition of the generated transformation channel matrix intoproducts between an orthogonal matrix Q and an upper triangular matrixR; calculating a signal corresponding to a component element of an Mthrow of a transmission column vector x, which includes M transmissionsignals as component elements, based on the calculated products Q^(H)yand the upper triangular matrix R; selecting a plurality of transmissionsignal candidates regarding the component element of the Mth row of thetransmission column vector x which are ranked in response to aninter-signal point distance with the signal calculated on aconstellation by carrying out a region decision on the constellationregarding the calculated signal; selecting a number of transmissionsignal candidates corresponding to predetermined parameters based on theinter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products QHy, the upper triangular matrix R andthe selected plural transmission signal candidates; and reproducing theM transmission signals by determining a unique set of the transmissionsignal candidates regarding the component elements of the Mth to 1strows of the transmission column vector x based on the sum total of theinter-signal point distances between the selected transmission signalcandidates and the signals.
 5. The signal processing method according toclaim 4, wherein the predetermined parameters include a ratio between anaverage value of the sum totals of the inter-signal point distances anda diagonal component element of the upper triangular matrix R.
 6. Thesignal processing method according to claim 4, wherein the predeterminedparameters include a ratio between a diagonal component element of the(M−1)th row of the upper triangular matrix R and a diagonal componentelement of the Mth row of the upper triangular matrix R.
 7. A receiverfor a wireless communication system which includes a transmitter whichtransmits a wireless signal using M transmission antennas and saidreceiver which receives the wireless signal using N reception antennas,M and N individually being integers equal to or greater than two, thereceiver comprising: a QR decomposition processor that performs QRdecomposition of a channel matrix between the transmitter and saidreceiver into products between an orthogonal matrix Q and an uppertriangular matrix R; a unitary transformer that calculates productsQ^(H)y between Q^(H) which is Hermite conjugate of the orthogonal matrixQ calculated by said QR decomposition processor and a reception columnvector y including N reception signals received by said receiver ascomponent elements; and a signal reproducer that calculates a signalcorresponding to a component element of the Mth row of a transmissioncolumn vector x, which includes M transmission signals as componentelements, based on the products Q^(H)y calculated by said unitarytransformer and the upper triangular matrix R, selects a plurality oftransmission signal candidates regarding component elements of the Mthrow of the transmission column vector x, which are ranked in response toan inter-signal point distance with the signal calculated on aconstellation by carry out a region decision on the constellationregarding the calculated signal, selects a number of transmission signalcandidates corresponding to predetermined parameters based oninter-signal point distances between the plural transmission signalcandidates and the signals from the plural transmission signalcandidates regarding component elements of the (M−1)th to 1st rows ofthe transmission column vector x by carrying out a region decision onthe constellation regarding signals corresponding to the componentelements of the (M−1)th to 1st rows of the transmission column vector xbased on the calculated products Q^(H)y, the upper triangular matrix Rand the selected plural transmission signal candidates, and reproducesthe M transmission signals by determine a unique set of the transmissionsignal candidates regarding the component elements of the Mth to 1strows of the transmission column vector x based on the sum total of theinter-signal point distances between the selected transmission signalcandidates and the signals.
 8. The receiver according to claim 7,wherein the predetermined parameters include a ratio between an averagevalue of the sum total of the inter-signal point distances and adiagonal component element of the upper triangular matrix R.
 9. Thereceiver according to claim 7, wherein the predetermined parametersinclude a ratio between a diagonal component element of the (M−1)th rowof the upper triangular matrix R and a diagonal component element of theMth row of the upper triangular matrix R.